← Back to Skills Marketplace
jinhuadeng

Xiaohongshu Ad Ops

by Koi · GitHub ↗ · v0.2.0 · MIT-0
cross-platform ✓ Security Clean
77
Downloads
0
Stars
0
Active Installs
2
Versions
Install in OpenClaw
/install xiaohongshu-ad-ops
Description
Plan, structure, and optimize Xiaohongshu (小红书) advertising workflows for lead generation, ecommerce, brand seeding, and conversion-oriented campaigns. Use w...
Usage Guidance
This skill appears coherent and safe to install. Before using: (1) only run the included script on non-sensitive sample input — the script reads the file you pass it; (2) don't provide account credentials or private data to the skill (none are required); (3) verify compliance with Xiaohongshu ad policies and local privacy rules for any ad copy or user data you plan to use; and (4) if provenance matters, consider confirming the publisher since the registry source is 'unknown'.
Capability Analysis
Type: OpenClaw Skill Name: xiaohongshu-ad-ops Version: 0.2.0 The skill bundle is a legitimate set of marketing templates and a helper script for Xiaohongshu (XHS) advertising operations. The Python script (xhs_ad_plan_brief.py) is a simple text parser that reads a local file and outputs a JSON summary; it contains no network calls, shell execution, or obfuscation. The SKILL.md and reference documents provide domain-specific guidance for ad copywriting and campaign structure without any evidence of malicious prompt injection or unauthorized data access.
Capability Assessment
Purpose & Capability
Name/description (Xiaohongshu ad ops) match the included resources (campaign playbooks, note templates, ad principles) and the small helper script that formats an ad brief; nothing in the package requests unrelated platform credentials or cloud access.
Instruction Scope
SKILL.md stays on-topic: it instructs generating ad plans, using bundled references, and optionally running the provided script with a local input file. It does not direct the agent to read arbitrary system files, access external endpoints, or exfiltrate data.
Install Mechanism
There is no install spec and no network download. The skill is instruction-first with a small local Python script; no archives or external installers are used.
Credentials
No environment variables, credentials, or config paths are required. The skill's needs (none) are proportional to its functionality.
Persistence & Privilege
always is false and the skill does not request persistent or elevated platform privileges. It does not modify other skills or system-wide settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install xiaohongshu-ad-ops
  3. After installation, invoke the skill by name or use /xiaohongshu-ad-ops
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.2.0
v2: added note templates, industry templates, landing/DM handoff, and review template for more practical ad ops delivery
v0.1.0
Initial release: Xiaohongshu ad planning, creative matrix, lead-gen playbook, and structured ad brief generator
Metadata
Slug xiaohongshu-ad-ops
Version 0.2.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 2
Frequently Asked Questions

What is Xiaohongshu Ad Ops?

Plan, structure, and optimize Xiaohongshu (小红书) advertising workflows for lead generation, ecommerce, brand seeding, and conversion-oriented campaigns. Use w... It is an AI Agent Skill for Claude Code / OpenClaw, with 77 downloads so far.

How do I install Xiaohongshu Ad Ops?

Run "/install xiaohongshu-ad-ops" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Xiaohongshu Ad Ops free?

Yes, Xiaohongshu Ad Ops is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Xiaohongshu Ad Ops support?

Xiaohongshu Ad Ops is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Xiaohongshu Ad Ops?

It is built and maintained by Koi (@jinhuadeng); the current version is v0.2.0.

💬 Comments